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แผนภูมิควบคุมโดยการสุ่มตัวอย่างแบบกลุ่มลำดับ
Received 5 April 2010 ; Accepted 2 July 2010
Innovative of an Instructional Design for Thai Industrial Education
Through Case-Based Reasoning
Weerayute Sudsomboon1* and Anusit Anmanatarkul2
1 Lecturer, Department of Mechanical Technology Education, Faculty of Industrial Education and
Technology, King Mongkut’s University of Technology Thonburi. 2 Assistant Professor, Department of Mechanical Technology Education, Faculty of Industrial Education
and Technology, King Mongkut’s University of Technology Thonburi.
* Corresponding Author, Tel. 0-2470-8525-6, 08-9477-6487, E-mail: [email protected]
1. Introduction
The main catalyst for this review deals with
methods, models, and strategies to retrieval, reuse,
revision, and retention in Case-based Reasoning
(CBR). In an automotive technology courses
practitioner’s often a complete description of the
target problem is assumed to be available in
advance. In adaptive industrial education systems an
experience approach can be used to deliver
profession course based on the both current learner
competence and on the competence of learners,
together with the experiences of using narratives
and teaching strategies. The suitability of a
profession course offering can be determined by
examining the learner’s feedback, explicit and
implicit knowledge [1], [2]. Moreover, a case-based
reasoning approach is proposed for analyzing,
identifying, presenting, and organizing potential
problems with profession courses by matching,
reusing, validating and stories cases, where cases
may be individual learner models, narratives or
individual content models.
This paper organized as follows: First authors
describe an overview of the existing literature of
CBR principles, methods, and apply is made within
a general scheme. Authors’ belief that best takes
place by using stories elicited from skill problem
solvers, indexed for the content models is necessary
to teach, and made available to learners in the form
of case libraries can provide a broader range of
problem solving than other strategy or tactic.
Furthermore, authors, address the characteristics of
a comprehensive CBR. With outline the summary of
the study for future research.
As automotive technology assumes an
increasingly dominant role in highly-performance,
technology literacy is becoming as essential as
students’ competency and the ability to service,
repair and diagnosis. In providing the fundamentals
of technological literacy, technology education
increases capability prepare to live and work in a
world of continuously evolving technologies.
Current automobiles are a challenge to service and
repair because of this advanced technology, but the
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แผนภูมิควบคุมโดยการสุ่มตัวอย่างแบบกลุ่มลำดับ
future automobile will be even more complicated
[3]. Hence, the Mechanical Technology Education
(MTE) Program, Faculty of Industrial Education
and Technology at King Mongkut’s University of
Technology Thonburi (KMUTT) is designing an
instructional design via learning innovation through
a constructivist approach. The MTE program is to
stress implementation of knowledge construct and
metacognition domain emphasizes the competency
in field of mechanical engineering and educational
technology. Derived from the concept of industrial
education is a terminology used more specifically in
this research to describe social demands that need
competency-based learning strategy for student
development. With collaborative efforts, enterprise
and university jointly design learning programs to
meet the demands of potential student as well as the
needs of social demand. Moreover, automotive
technology changes affect adjustments in, and
instructional system and design of, students’
competencies. Thus, MTE program should use a
suitable competency analysis model in order to
establish the competency connation and standards in
every domain. In this issue, the development of an
automotive technology competency analysis profile
model is actually an important requirement for
undergraduate mechanical technology education
students.
2. Completing Perspectives
Learning is a constructive, cumulative, self-
regulated, goal-oriented, situated, collaborative, and
individually different process of knowledge building
and meaning construction [4]. The constructivist
view of learning also influences the role of teachers.
The main task that teachers are assumed to perform,
according to constructivists, is no longer the
transmission of knowledge, but the facilitation
and coaching of learning [5], [6]. CBR is a
problem-solving approach that relies on past similar
cases to find solutions to problems [7], and prepare
professionals to deal with ill-defined and
ill-structured problems by exposing them to stories
generated in the workplace [2]. Leake [8] suggested
that CBR principle is based on an analogy to the
human task of “mentally searching for similar
situations which happened in the past and reusing
the experience gained in those situations”.
Therefore, a query describing a target problem is
natural, analogical reasoning cycle, stories of
concrete problem- solving experience retrieved from
dynamic memory (analogs) are adapted to
interpreting and solving a new problem [1], [7],
[9]-[13]. The problem solving experience affects
dynamic memory by generating a solution, new
story which is used in future.
As a result, dynamic memory involves with the
integration of each new story. The CBR principle is
based on an analogy to the human task of “mentally
searching for similar situations which happened in
the past and reusing the experience gained in those
situations” [8]. In CBR, all the problems are
represented as cases, which were defined by
Kolodner and Leake [12] as: “A case is a
contextualized piece of knowledge representing on
experience that teaches a lesson fundamental to
achieving the goals of the reasoner.” A case usually
has two major parts: the problem itself with the
context describing the environments it should be
retrieved; and the solution of the problem or the
lesson it will teach. CBR can be seen as a 4 RE’s
cyclic process: Retrieve Reuse, Revise and Retain.
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This cycle is cited in Figure 1 can be represented as
follows:
1. Retrieve: the system searches and retrieves
the case(s) most similar to the problem case,
according to a predefined similarity measure.
2. Reuse: the user evaluates it in order to
decide if the solution retrieved is applicable to the problem.
3. Revise: if it cannot be reused, the solution is
revised (adapted) manually (by the user) or
automatically (by the CBR system).
4. Retain: the confirmed solution is retained
with the problem, for future reuse, as a new case in
the database.
CBR methodology usually concerns the
following issues so that different components can
work co-operatively, contributing to efficient and
effective system performance. CBR has been very
successful in a wide range of problems over the last
decade [7]. A vast amount of work has been carried
out concerning a wide range of issues and different
techniques in CBR [1]. Although the CBR cycle is a
retrieve-evaluate-adapt-learn process, a CBR
system may very well implement only the retrieve
step, as this is the expression of the concept of reuse
of experience.
Additionally, the difference between a database
search and CBR retrieval is that the latter employs
searching mechanisms that are based on
classification and decision tree algorithms, or on
assessment of the similarity of cases using
predefined similarity measures. This section,
instead, will highlight the fact that unlike
knowledge-based systems, where the problem
solving algorithms are expressed in rules, CBR
systems deal with case specific knowledge and do
not require that the domain must be modeled in rules.
Figure 1 The CBR cycle [1]
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3. Analyzes of Current, Trends and Issues
Interpretive CBR involves reusing previous
cases for classification tasks, such as diagnosis,
evaluation and prediction – applications can be
found in, for example, medical diagnosis and
helpdesk situations (i.e., Symptom-based diagnosis).
Using CBR for problem solving, on the other hand,
involves reusing and adapting solutions of past,
similar problems in order to solve new problems.
Most contemporary models of instruction, including
anchored instruction [14], problem-based learning
[15], open-ended learning environments [16],
constructivist learning environments [17], goal-
based scenarios [18] share an essential
characteristic. The learning outcome for each is
problem solving. That is, each of these models
supports learning how to solve some kind of a
problem. The emphasis on problem solving in the
field of instructional design has increased. Problem
solving is a complex, multifaceted, and poorly
understood kind of learning.
According to Jonassen [17], [18] has attempted
to articulate different kinds of problem solving and
different learning and instructional requirements for
each. However, insufficient advice is available to
instructional designers to help them to design and
develop learning and instructional supports for
every kind of problem solving. In this paper, we
describe the application of CBR to decision support
for Automotive Suspension and Transmission
systems field subjects in Industrial Education by
using stories to support problem solving. This study
examined how stories, in the form of a CBR,
affected undergraduate novices’ abilities to solve
complex and ill-structured problems. It is thought
that in order to develop professional skilled to deal
with the complexity of workplace situation (i.e. to
deal with ill-structured problems) they should be
supported by providing stories generated at the
workplace itself.
In professional practice, situations abound with
indeterminacy, value conflict, and conflicting views.
In these ill-structured contexts, the successful
practitioner must be able to “choose among multiple
approaches to practice or devise his own way of
combining them”. Unfortunately, novices in schools
are only allowed to work on problems that are
decontextualized and well-structured, while
problems in everyday and professional contexts are
complex ill-structured [17] - [19]. CBR assumes that
cases in the form of stories are useful for supporting
problem solving focus on the novice’s attention on
what is an important, making available idea on how
to move forward, and giving grounds for reassessing
the consequences of their decision or actions [20].
The process of understanding and solving new
problems in terms of previous experiences has three
parts: recalling old experiences, interpreting the new
situation in terms of the old experience based on the
lessons that were learned from the old experience,
or adapting the old solution to meet the needs of the
new situation [20]. Given the lack of previous
experiences among novices, substitute experiences
available through a case library are expected to
augment learners’ repertories of experiences by
connecting to the experiences of others (experts),
forewarning us of potential problems, realizing what
to avoid, and foreseeing the consequences of
decisions or actions. Reasoning from stories or cases
supports ‘inferences necessary for addressing the
kinds of ill-defined or complex problems that arise
in the workplace, at school, and at home’.
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4. Examples of Research in CBR
In an ethnographic study of problem solving
among refrigeration service technicians, Henning
[21] found that stories served as a mechanism for
promoting an ongoing discourse between
technicians, machines, products and people. Stories
afforded technicians a means to form and express
their initiation. By being able to tell stories to their
co-workers, technicians, particularly the newer ones,
were able to form and strengthen the bonds that give
cohesiveness to their community of practice.
Technician shared stories about initiation, identity
formation, their sense of pride, and in general about
the drama of facing responsibility, and unusual and
difficult situations. These stories reinforced the
technicians’ identity, which contributed to their
further participation in the community they were
continuously building. On the other hand,
Lave and Wenger [22] found stories to be
critical also for initiating new members into a
practice. While studying apprentices in their work
setting, they found that “apprenticeship learning is
supported by conversations and stories about
problematic and especially difficult cases”. In these
setting, stories were used as “communal forms of
memory and reflection”. In this paper, these studies
in Professional contexts have shown that narrative
dialogue of reflection and interpretation sustained by
these practitioners is how “experience is transformed into
pedagogical content knowledge” [22].
Jonassen and Hernandez-Serrano [23]
proposed that the result on ‘The effects of case
libraries on problem solving’. From a CBR
perspective, the problem-solving approach involves
the research process.
They found that the effects of providing access
to a case library of related stories while undergraduates
solved ill-structured problems. While solving
complex food product development problems,
the experimental group accessed experts’ stories of
similar, previously solved problems; the comparable
group accessed fact sheets (expository representation
of stories’ content); and the control group accessed
text selected at random from a textbook
dealing with issues unrelated to the stories. On
multiple-choice questions assessing processes
related to problem solving (prediction, inferences,
explanations, etc.), experimental students
out-performed the comparable and control groups.
Performance on short-answer questions also
assessing problem-related skills was not
significantly different, in part because of
test fatigue. Analysis of interviews identified a
number of factors that students used in deciding
how to apply their study strategies, including causal
factors, grounding phenomenon, grounding in
context, and outcomes.
Moreover, Althuizen and Wierenga [24] found
that CBR as a support technology for sales
promotion (SP) decisions. CBR-systems try to
mimic analogical reasoning, a form of human
reasoning that is likely to occur in weakly-structured
problem solving, such as the design of sales
promotions. In an empirical study, it finds
evidence that use of the CBR-system improves
the quality of SP-campaign proposals. Creativity,
in turn, is positively related to the (practical)
usability of a proposal. The results suggest
that the CBR-system is most effective when
it is used as an idea-generation tool that
reinforces the strength of divergent (creative)
thinkers.
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5. Contextualization in CBR
5.1 Traditional Learning VS Case-based Reasoning
The skilled and techniques of traditional
expertise, particularly as they are being taught in
schools, do not match the complexity found in the
fields of medicine, management, engineering and
many other professions. Novices in industrial
education are trained only to study on content-based
that are, by nature, decontextualized and well
structured, while problems in everyday and
professional contexts are complex and ill-structured
[17] – [19]. Unlike well-structured problems
encountered in formal education, ill-structured
problems do not have single solutions, are
open-ended, are composed of many sub problems;
they frequently have many possible solutions paths;
and they posses no clear beginning or end [17], [25],
[26]. Therefore, the skills required to solve
well-structured problem are different than those
required to solve ill-structured problems. Authors’
believe that instructional materials supporting
ill-structured problem solving skills should
incorporate ‘case that represent probable real-world
problems in the domain, that is, that are authentic’
[17].
Koul [27] explained that stories are the oldest
and most natural form of sense making. Stories are
the “mean [by] which human beings give meaning
to their experience of temporality and personal
actions” (as cited in Polkinghorne [28]. Cultures
have maintained their existence through different
types of stories, including myths, fairy tales, and
histories. Humans appear to have an innate ability
and predisposition to organize and represent their
experiences in the form of stories. One reason for
that proclivity is that stories require less cognitive
effort than exposition because of the narrative form
of framing experience [29]. To be part of a culture,
it is necessary to be connected to the stories that
abound in the culture. Telling stories has function: It
is a method of negotiating meanings [30], [31] that
allows us to enter into others’ realms of meaning
through the messages they utter in their stories [28].
5.2 The Domain Knowledge Acquisition for CBR
The type of knowledge includes abstract/
general knowledge and concrete/specific knowledge
[1], [7], [31]. Training in automotive technology
involves both type of knowledge [32]. The CBR
process is centered on concrete/specific knowledge,
but execution and outcomes of the process often
include abstract/general knowledge. This abstract/
general knowledge is made in
1. Established procedures applied to solving
the problems.
2. Indexes of cases.
3. Lessons learned from a problem-solving
episode and included in a case.
CBR can be implementing to adapt both
case-based (involving concrete/specific knowledge)
and schema- mediated (involving abstract/general
knowledge) [1], [33]. Research dealing with
CBR-inspired applications in education and training
often concentrates on the development based on
CBR products [11], [13] and could be classified into
four categories:
1. CBR-inspired learning environments.
2. Supports for reflection and interpretation of
personal experience.
3. Case libraries.
4. Hybrids combining supports for reflection
and interpretation with libraries.
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Case libraries as learning resources rely on the
available of multiple cases and this need has led to
the development [13], [33], [36]. As an integral
component of a library, the library’s indexing
scheme enables transfer and application of
knowledge stored in the library to resolving new
problem situations [37]. Richmond [38] summarized
that the sample of case libraries serving as learning
resources are presented in Table 1.
whether or not libraries continued to be used.
However, because continued use might reflect
inertia more than efficacy, other indicators must be
used. Two general findings as the efficacy of
libraries relate to building cases and comparing/
contrasting cases [31] – [36].
In addition, students can learn as much or even
more from constructing cases as form simply using
them, assuming a case structure is specified. Second,
comparing and contrasting multiple cases for relevance
to problem-solving can be more effective learning
experience than applying a single case.
5.3 CBR as a Technique for Instructional Design
on Automotive Technology Courses
In order to analyze stories using CBR, it is
necessary first to elect and capture relevant stories
about previously solved problems from the
practitioners [2] commended for the following four
activities:
1. Identify skilled practitioners in the domain.
2. Show the practitioners the practitioners the
problem for which you are seeking support.
3. Ask the practitioners if they can remember
any similar problems that they have solved. If they
can (and they usually can), allow them to tell a story
about the problem without interruption. Audiotape
or (better yet) videotape their recounting of the
story. Following the telling of the story, analyze it
with the practitioner. Work with the practitioner to:
1. Identify the problem goals and expectations.
2. Describe the context in which the problem
occurred.
3. Describe the solution that was chosen.
4. Describe the outcome of the solution. Was it
successfully? Was it a failure? Why? Identify the
Table 1 Example of Case Libraries Serving as
Learning Resources Description Type of Problems Solved
Archie-2 Architectural design issues
STABLE Computer programming skills
Case Application Suite Application of cases
Parent-teacher conferences Analysis of what happens in a conference
Technology integration Analysis of technology integration
USDOE PT3/KITE database Increase technology integration
Turfgrass management Learn domain skills and knowledge
Medical case library Diagnostic and casual reasoning skills
Susie Issues of sustainable technology and development
In training to solve problems in automotive
technology, case library can go beyond simply a
library to promoting also contributing to knowledge
storage much more than another CBR tool. The
library contained case about automotive problems,
such as automotive engine systems, suspension
systems, transmission systems, body electrical
systems, electronic control systems, air conditioning
systems, and etc. These practitioners acquired
domain content by using and contributing to the
library. One indicator of libraries’ efficacy is
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points that each story makes (i.e., the lessons that it
can teach).
4. Decide what the stories teach. The final step
in the analysis process is to index the stories.
Indexing stories is the primary analytic activity in
the CBR process. It is described here because it is
an important task-analysis method. Indexing a
number of stories will also provide useful prompts
to use when eliciting stories from other
practitioners.
6. Implications
In order to accomplishment, Jonassen and
Hernandez-Serrano [2] explained that a method for
collecting stories from practitioners:
1. Identify skilled practitioners who have
solved problems similar to the one that are being
presented to the students;
2. Show the practitioners the problems, one
problem at time. Ask the practitioners if they can
remember any similar problems that they have
solved. If they can, allow them to tell a story about
the problem without interruption;
3. Following the telling of the story, analyze it
with the practitioner. Work with practitioner to
identify the problem goals, the context in which the
problem goals, the context in which the problem
occurred, the solution that was chosen, and why, and
the outcome of the solution. It is important to
identify the points that each story makes, that is, the
lessons that it can teach;
4. Following the story telling, the cases should
probably be indexed, a process that is set out in
Kolodner [7]. And
5. Turf case library at Penn State: http://turfgrass.
cas.psu.edu/caselibrary/.
In addition, the effectiveness of learning
through CBR can be proposed a prescriptive design
for a new Bachelors of Thai Industrial Education
program in modernize studies. The process of CBR
involves each of the following steps:
1. An encountered problem (the new case)
appearance during operate in the workplace prompts
the reasoner to retrieve cases from the memory.
2. To reuse the old case (i.e., interpret the new
in terms of old), which suggests a solution.
3. If the suggested solution does not work, then
the old and or new cases are revised.
4. When the effectiveness is confirmed, then
the learned case is retained for later use [2].
Stories can be indexed in two ways, (a) the
more common method is through direct input by the
human user, who must appropriately index the
stories order to make them accessible in a case
library. (b) Stories can also be indexed for case
libraries by adapting and reindexing already existing
cases to new situations. For each case, identify the
relevant indexes that would allow cases to be
recalled in the situation. Choose from among the
following indexes, most of which were suggested by
Kolodner [7]:
1. Problem-Situation-Topic Indexes
1.1 What are the goals-sub goals-intensions to
be achieved in solving the problem or explaining the
situation? What constraints affect this goal?
1.2 Which features of the problem situation are
the most important and what is the relationship
between its parts?
1.3 What plans are develop for accomplishing
the goal?
2. Appropriate Solution Indexes
2.1 What solution is use?
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2.2 What activities are involved in accomplishing
the solution?
2.3 What is the reasoning steps used to derive
the solution?
2.4 How do you justify the solution?
2.5 What expectations do you have about results?
2.6 What acceptable, alternative solutions, is
suggest but not choose?
2.7 What unacceptable, alternative solutions, is
not choose?
3. Appropriate Outcomes Indexes
3.1 Is the outcome fulfilling?
3.2 Are expectations violating?
3.3 Is the solution a success or failure?
3.4 Can you explain why any failures occur?
3.5 What can you do to avoid the problem?
The result of the elicitation and indexing of
stories from practitioners will provide more than
enough information to design almost any form of
problem-based learning environment. Although this
information is activity oriented, links to theory will
be obvious in the data. In fact, indexes that were
produced to analyze each teacher story included the
type of conference, classroom placement, reason for
the conference plan, and result of the conference and
so on. Stories can provide activity-based descriptions
rather than a content description, linking to content
is easy. The indexes is nearly all form of problem-based
learning begin with a problem and teach the content
in the context of the problem. With case-based reasoning,
the task analysis also begins with the problem.
CBR puts forward a paradigmatic way to attack
artificial intelligence (AI) issues, namely problem solving,
learning, usage of general and specific knowledge,
combining different reasoning methods, etc. In particular
CBR emphasizes problem solving and learning as
problem solving uses the results of past learning
episodes while problem solving provides the backbone
of the experience from which learning advance [1].
7. Summary and Future Research
In this paper, authors outlined an overview of
the existing literature of CBR principles, methods,
and applies is made within a general scheme.
Authors believe that best takes place by using
stories elicited from skill problem solvers, indexed
for the content models is necessary to teach, and
made available to learners in the form of case
libraries can provide a broader range of problem
solving than other strategy or tactic. An important
issue is characteristics of a comprehensive CBR tool
collected in the cases and used in the adaptation
process and the validation process to insure quality
and efficiency [39], [40].
This paper discussion provides a theoretical
explanation that might, in contractdiction to CBR
process, improve problem-solving and case-based
reasoning skills. The pre-service teachers
(undergraduate mechanical technology education
students) will be highly adaptable and creative when
integrating learning experience in the workplace.
Educators would greatly benefit from the
proliferation of this learning innovation.
A comprehensive CBR should support
application of the CBR processes to ill-structured
problem solving are: (a) Retrieve, (b) Reuse, (c)
Revise, and (d) Retain [1]. Problem solving can be
of two types: either an algorithmic-type problem-
solving, or the more challenging ill-structured-type
problem-solving. Algorithmic problem-solving
skills follow a set pathway to the problem solution.
In contrast, ill-structured problem-solving skills
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follow a circuitous and unpredictable pathway to
solution, and rank at the highest level of intellectual
skills. Moreover, a case library is simply an indexed
database of solved cases, each including of the
problem, solution, and outcome linked to support
materials [41].
Finally, a CBR should include the capacity for
collecting cases from domain content experts. A
defining tenet of CBR is that the novice
practitioners of a domain can look to the skilled
performance of experienced practitioners of the
domain for guidance in resolving domain issues
[38], [42], [43] in Table 2.
The plan for the future research is to construct
a number of the idea and challenge outlined with
Table 2 Seven Theory as a Tool for Research-based
Design Requirements
Title Requirement
1. Integrated model Reflect a model of cognition that integrates reasoning, understanding and learning, and dynamic memory
2. Four major steps Support performing four major steps of the CBR process
3. Ill-structured problem solving
Support the ill-structured problem-solving process
4. Two major parts Consist of two primary parts: supports for reflection and interpretation of experience and a case library for storing these interpretations
5. Integration A versatility for ready integration into a range of domains
6. Case construction and contribution
Support constructing cases and contributing them to the case library
7. Expert modeling Support contribution of cases by domain experts
concept maps. The goals of the research should be
taken into account when solving the problem what is
a good explanation in any situation. For enhancing
the effectiveness of adaptive features of CBR
approach, and implement them in a research project
between the Office of National Research Council of
Thailand (NRCT) and Department of Mechanical
Technology Education, Faculty of Industrial
Education and Technology at King Mongkut’s
University of Technology Thonburi (KMUTT).
Authors are hoped that this approach will yield
greater and more focused development to the
undergraduate mechanical technology education
students on automotive technology courses enabling
them to improve the learning experience for the
learner.
8. Acknowledgements
The authors would like to thank especially like
to express gratitude for the contribution of the
Office of National Research Council of Thailand
(NRCT) for financially supporting this research in
Phase I-II (B.E. 2553-2554), Dr. Ravinder Koul,
Associate Professor of Science Education, Great
Valley Graduate School of Professional Studies,
Pennsylvania State University, Malvern, USA, and
Dr. Doug Richmond, Virginia Polytechnic Institute
and State University.
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